tailwindlabs / tailwindcss
github.com·21h ago
Developer Tools
Databricks Blog·Jun 26·5 min read
A Decision Framework for ETL Migration to Databricks
TL;DR
Data teams face challenges in migrating ETL (Extract, Transform, Load) processes to Databricks due to differing opinions on the approach. A decision framework was developed to guide teams in selecting between Lakehouse, Spark Declarative Pipelines, or PySpark for their migration.
✦ Why It Matters
Engineers can use this framework to make informed decisions on ETL migration strategies to Databricks.
Key Takeaways
- Evaluate each workload individually for optimal migration.
- Consider using Lakehouse for SQL-heavy processes.
- Utilize Serverless for variable workloads.
- Consolidate metadata and permissions during migration.
Full Summary
Organizations often struggle with ETL migration to Databricks, especially when dealing with numerous stored procedures and scattered permissions. To address this, a decision framework was created to help teams choose the most suitable migration path: Lakehouse (Databricks SQL), Spark Declarative Pipelines, or PySpark.
The framework emphasizes evaluating each workload on its own merits, allowing for a tailored approach that considers factors like metadata and permissions. For SQL-heavy teams, migrating to Lakehouse offers a straightforward path, leveraging Photon-accelerated SQL warehouses for performance.
Serverless options are available for unpredictable workloads, while Classic options cater to steady workloads needing specific controls. This structured approach not only streamlines the migration process but also enhances the overall efficiency of data operations.